Coordinated version control system, method, and recording medium for parameter sensitive applications

ABSTRACT

Version vector-based rules are used to facilitate asynchronous execution of machine learning algorithms. The method uses version vector based rule to generate aggregated parameters and determine when to return the parameters. The method also includes coordinating the versions of aggregated parameter sets among all the parameter servers. This allows to broadcast to enforce the version consistency; generate parameter sets in an on-demand manner to facilitate version control. Furthermore the method includes enhancing the version consistency at the learner&#39;s side and resolving the inconsistent version when mismatching versions are detected.

The present Application is a Continuation Application of U.S. patentapplication Ser. No. 15/169,471, filed on May 31, 2016, which is aContinuation Application of U.S. patent application Ser. No. 15/169,411,filed on May 31, 2016, the entire contents of which is incorporatedherein by reference.

BACKGROUND

The present invention relates generally to a coordinated version controlsystem, and more particularly, but not by way of limitation, to acoordinated version control system for using a rule-based approach tofacilitate asynchronous execution, introducing a server side coordinateversion control to achieve version consistency, and introducing alearner side version enhancement to further enforce the versionconsistency.

Many existing machine learning algorithms partition the parameters intomultiple disjoint parameter sets and ask distributed parameter serversto aggregate different sets. Meanwhile, contemporary parameter serversadopt asynchronous execution to tolerate slow learners.

Conventionally, in asynchronous execution (on a server side), theconstraint that aggregation only happens after all parameters arecollected is relaxed. Instead, server can carry out aggregation when aproportion of parameters are received, then send back to clients. Also,in asynchronous execution (on a client side), a learner can continuetraining without waiting for the arrival of the latest aggregatedparameters.

However, there is a technical problem in the conventional techniquesthat, as a result of this combined design of the server side and theclient side, the conventional techniques may create undesirablemismatches between the versions of aggregated parameter sets returned bydifferent servers. Such an issue can lead to slow convergence, or even acomplete inability to converge in certain cases.

SUMMARY

The inventors have considered the newly identified technical problem andrealized that there is a significant need for a rule-based approach tofacilitate asynchronous execution, introducing a server side coordinateversion control to achieve version consistency, and introducing alearner side version enhancement to further enforce the versionconsistency.

In an exemplary embodiment, the present invention can provide acoordinated version control system including a parameter server having aplurality of parameter sets and a plurality of learners communicatingwith the plurality of parameter sets, the system including a leaderparameter collecting circuit configured to parameter set data for aleader parameter set from the plurality of learners, a followerparameter collecting circuit configured to collect parameter set datafor a follower parameter set from the plurality of learners, a leaderaggregated parameter generating circuit configured to generate a newleader version of a leader aggregated parameter set based on the leaderparameter set data, an event broadcasting circuit configured to generatea broadcast event indicating that the new version of the aggregatedparameter set has been generated, a broadcast detecting circuitconfigured to detect the broadcast event, a checking circuit configuredto check if a version of the follower parameter set matches the leadernew version of the aggregated parameter set based on the broadcastdetecting circuit detecting the broadcast event, and a followeraggregated parameter generating circuit configured to generate a newfollower version of a follower aggregated parameter that matches the newleader version of the leader aggregated parameter set.

Further, in another exemplary embodiment, the present invention canprovide a coordinated version control method for a parameter serverhaving a plurality of parameter sets and a plurality of learnerscommunicating with the plurality of parameter sets, the method includingfirst collecting parameter set data for a leader parameter set from theplurality of learners, second collecting parameter set data for afollower parameter set from the plurality of learners, generating a newleader version of a leader aggregated parameter set based on the leaderparameter set data, broadcasting a broadcast event indicating that thenew version of the aggregated parameter set has been generated, checkingif a version of the follower parameter set matches the leader newversion of the aggregated parameter set based on the broadcast eventbeing detected, and generating a new follower version of a followeraggregated parameter that matches the new leader version of the leaderaggregated parameter set.

Even further, in another exemplary embodiment, the present invention canprovide an asynchronous execution facilitation system including aparameter server having a plurality of parameter sets and a plurality oflearners communicating with the plurality of parameter sets, the systemincluding a server checking circuit configured to check if a new versionof an aggregated parameter set can be generated based on an intermediateversion vector for each of the plurality of learners, an epoch updatechecking circuit configured to check a number of updated epochs based ona number of the plurality of learners that have pushed updated parameterset data to the parameter server according to the intermediate versionvector, and an aggregation triggering circuit configured to generate thenew version of the aggregated parameter set if the number of updatedepochs is equal to the number of the plurality of learners that havepushed the updated parameter set data to the parameter server multipliedby a predetermined threshold portion of the plurality of learners.

Also, in another exemplary embodiment, the present invention can providea version enforcement system including a parameter server having aplurality of parameter sets and a plurality of learners communicatingwith the plurality of parameter sets, the system including a parametercollecting circuit configured to collect the plurality of parameter setsfrom the parameter server, a version examining circuit configured todetermine versions of each of the parameter sets, a mismatch detectingcircuit configured to detect if a mismatch exists between the versionsof each of the parameter sets, a determining circuit configured todetermine a correct version for each of the parameter sets to generateif the mismatch detecting circuit detects the mismatch, a requestingcircuit configured to request that each of the parameter sets generatethe correct version, a success checking circuit configured to verifythat the parameter sets each generate the correct version in response tothe request by the requesting circuit, and a local parameter updatingcircuit configured to automatically update the local parameters forcomputation by a learner with the correct version of the aggregatedparameter sets.

Moreover, in another exemplary embodiment, the present invention canprovide a version enforcement method including a parameter server havinga plurality of parameter sets and a plurality of learners communicatingwith the plurality of parameter sets, the method including collectingthe plurality of parameter sets from the parameter server, determiningversions of each of the parameter sets, detecting if a mismatch existsbetween the versions of each of the parameter sets, determining acorrect version for each of the parameter sets to generate if thedetecting detects the mismatch, requesting that each of the parametersets generate the correct version, checking that the parameter sets eachgenerate the correct version in response to the request by therequesting circuit, and automatically updating the local parameters forcomputation by a learner with the correct version of the aggregatedparameter sets.

Additionally, in another exemplary embodiment, the present invention canprovide an asynchronous execution facilitation system including aparameter server having a plurality of parameter sets and a plurality oflearners communicating with the plurality of parameter sets including aserver checking circuit configured to check each pull request to theparameter server for an immediate version vector and a local epoch ofthe learner and an aggregation returning circuit configured to comparethe immediate version vector pulled by the parameter server with acomparison condition such that if the comparison condition is satisfied,the aggregation returning circuit causes the parameter server to returna new aggregated parameter to the learner, the comparison conditionbeing a comparison of the immediate version vector to a current versionof the aggregated parameter set of the parameter server.

There has thus been outlined, rather broadly, an embodiment of theinvention in order that the detailed description thereof herein may bebetter understood, and in order that the present contribution to the artmay be better appreciated. There are, of course, additional exemplaryembodiments of the invention that will be described below and which willform the subject matter of the claims appended hereto.

It is to be understood that the invention is not limited in itsapplication to the details of construction and to the arrangements ofthe components set forth in the following description or illustrated inthe drawings. The invention is capable of embodiments in addition tothose described and of being practiced and carried out in various ways.Also, it is to be understood that the phraseology and terminologyemployed herein, as well as the abstract, are for the purpose ofdescription and should not be regarded as limiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary aspects of the invention will be better understood fromthe following detailed description of the exemplary embodiments of theinvention with reference to the drawings.

FIG. 1 exemplarily shows a block diagram illustrating a configuration ofan asynchronous execution facilitation system 100.

FIG. 2 exemplarily shows a high level flow chart for an asynchronousexecution facilitation method 200.

FIG. 3 exemplarily shows a first depiction of learners and serverscommunicating parameters at different epochs.

FIG. 4 exemplarily shows a second depiction of learners and serverscommunicating parameters at different epochs.

FIG. 5 exemplarily shows a block diagram illustrating a configuration ofa coordinated version control system 500.

FIG. 6 exemplarily shows a high level flow chart for a coordinatedversion control method 600.

FIG. 7 exemplarily shows a block diagram illustrating a configuration ofa version enforcement system 700.

FIG. 8 exemplarily shows a high level flow chart for a versionenforcement method 800.

FIG. 9 depicts an exemplary embodiment of the coordinated versioncontrol system 500.

FIG. 10 depicts a cloud computing node 10 according to an embodiment ofthe present invention.

FIG. 11 depicts a cloud computing environment 50 according to anotherembodiment of the present invention.

FIG. 12 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

The invention will now be described with reference to FIGS. 1-12, inwhich like reference numerals refer to like parts throughout. It isemphasized that, according to common practice, the various features ofthe drawing are not necessarily to scale. On the contrary, thedimensions of the various features can be arbitrarily expanded orreduced for clarity. Exemplary embodiments are provided below forillustration purposes and do not limit the claims.

With reference now to FIG. 1, the asynchronous execution facilitationsystem 100 includes a server checking circuit 101, an epoch updatechecking circuit 102, an aggregation triggering circuit 103, and anaggregation returning circuit 104. The asynchronous executionfacilitation system 100 includes a processor 180 and a memory 190, withthe memory 190 storing instructions to cause the processor 180 toexecute each circuit of an asynchronous execution facilitation system100. The processor and memory may be physical hardware components, or acombination of hardware and software components.

With reference now to FIG. 5, the coordinated version control system 500includes a leader parameter collecting circuit 501, a leader aggregatedparameter generating circuit 502, an event broadcasting circuit 503, aleader pull requesting circuit 504, a follower parameter collectingcircuit 505, a broadcast detecting circuit 506, a checking circuit 507,a follower aggregated parameter generating circuit 508, and a followerpull requesting circuit 509. The coordinated version control system 500includes a processor 580 and a memory 590, with the memory 590 storinginstructions to cause the processor 580 to execute each circuit ofcoordinated version control system 500. The processor and memory may bephysical hardware components, or a combination of hardware and softwarecomponents,

With reference now to FIG. 7, the version enforcement control system 700includes a parameter collecting circuit 701, a version examining circuit702, a mismatch detecting circuit 703, a local parameter updatingcircuit 704, a determining circuit 705, a requesting circuit 706, aretrying circuit 707, a success checking circuit 708, and a time-outdetermining circuit 709. The version enforcement control system 700includes a processor 780 and a memory 790, with the memory 790 storinginstructions to cause the processor 780 to execute each circuit of theversion enforcement control system 700. The processor and memory may bephysical hardware components, or a combination of hardware and softwarecomponents.

Although the asynchronous execution facilitation system 100, thecoordinated version control system 500, and the version enforcementsystem 700 include various circuits, it should be noted that theasynchronous execution facilitation system 100, the coordinated versioncontrol system 500, and the version enforcement system 700 can includemodules in which the memory 190 (590, 790) stores instructions to causethe processor 180 (580, 780) to execute each module of asynchronousexecution facilitation system 100, the coordinated version controlsystem 500, and the version enforcement system 700.

Also, each circuit can be a stand-alone device, unit, module, etc. thatcan be interconnected to cooperatively produce a transformation to aresult.

With the use of these various circuits, the asynchronous executionfacilitation system 100, the coordinated version control system 500, andthe version enforcement system 700 may act in a more sophisticated anduseful fashion, and in a cognitive manner while giving the impression ofmental abilities and processes related to knowledge, attention, memory,judgment and evaluation, reasoning, and advanced computation. That is, asystem is said to be “cognitive” if it possesses macro-scaleproperties—perception, goal-oriented behavior, learning/memory andaction—that characterize systems (i.e., humans) that all agree arecognitive.

Cognitive states are defined as functions of measures of a user's totalbehavior collected over some period of time from at least one personalinformation collector (including musculoskeletal gestures, speechgestures, eye movements, internal physiological changes, measured byimaging circuits, microphones, physiological and kinematic sensors in ahigh dimensional measurement space) within a lower dimensional featurespace. In one exemplary embodiment, certain feature extractiontechniques are used for identifying certain cognitive and emotionaltraits. Specifically, the reduction of a set of behavioral measures oversome period of time to a set of feature nodes and vectors, correspondingto the behavioral measures representations in the lower dimensionalfeature space, is used to identify the emergence of a certain cognitivestate(s) over that period of time. One or more exemplary embodiments usecertain feature extraction techniques for identifying certain cognitivestates. The relationship of one feature node to other similar nodesthrough edges in a graph corresponds to the temporal order oftransitions from one set of measures and the feature nodes and vectorsto another. Some connected subgraphs of the feature nodes are hereinalso defined as a cognitive state. The present application alsodescribes the analysis, categorization, and identification of thesecognitive states by further feature analysis of subgraphs, includingdimensionality reduction of the subgraphs, for example by graphicalanalysis, which extracts topological features and categorizes theresultant subgraph and its associated feature nodes and edges within asubgraph feature space.

Although as shown in FIGS. 10-12 and as described later, the computersystem/server 12 is exemplarily shown in cloud computing node 10 as ageneral-purpose computing circuit which may execute in a layer theasynchronous execution facilitation system 100, the coordinated versioncontrol system 500, and the version enforcement system 700 (FIG. 12), itis noted that the present invention can be implemented outside of thecloud environment.

It is noted that a parameter server is, for example, a centralized placefor learners to get latest aggregated parameters. The parameter servercollects the parameters from multiple learners, conducts aggregation,and sends the aggregated values back to the learners. Learnerscommunicate with the parameter servers through push/pull interfaces. A“push” is defined as the learners pushing computations back to theserver, whereas a “pull” is defined as the learners pulling compiledcomputations from the server to perform a new computation at a nextepoch.

Referring to FIG. 1 and the asynchronous execution facilitation system100, the server checking circuit 101 checks the aggregation conditionbased on an intermediate version vector. The aggregation condition isbased on the number of updated epochs.

The epoch update checking circuit 102 checks the number of updatedepochs (A) based on the number of learners (N) that have pushed theupdated parameters to the server.

The aggregation triggering circuit 103 triggers for the server toaggregate the pulled parameters from the learners when the number ofupdated epochs equals the number of learners multiplied by a portion (p)of the learners (e.g., when Δ=N×p). “p” is between 0 and 1 where 1indicates synchronous execution. In other words, the server aggregatesthe pulled parameters from the learners when a proportion of thelearners have completed the current calculations at that particularepoch. In this manner, the aggregation triggering circuit 103 canparticularly set the value “p”, thereby to create a rule for howtolerant the system 100 can be to slower (or faster) learner sidecomputations.

For example, if the system 100 wants to be nearly (i.e., substantially)synchronous, the aggregation triggering circuit 103 can preferably set“p” to be near (i.e., 0.97, 0.98, 0.99, etc.) a value of 1 such that alllearners need to complete all computations at the particular epochbefore the server completes aggregation and pushes the aggregation tothe learners for a new epoch to begin.

Alternatively, the aggregation triggering circuit 103 can set “p” to bea smaller value such that the system 100 acts in a more asynchronousmanner in that the lower the portion of the learners that need to pushthe completed calculation to the server before the server performs anaggregation is less, such that the server 100 can advance to additionalepochs with less learners completing the computations of the currentepoch.

Referring to FIG. 3, for example, if “p” is set to 0.6, when 2 of the 3learners complete the calculations for the parameter sets 1, 2, and 3 atepoch 2, the aggregation triggering circuit 103 will trigger the serverto aggregate the parameter sets. For example, as shown in FIG. 3 atepoch 3, parameter set 1 has version (2, 2, 2), parameter set 2 has twoversions of (2, 1, 2) and (2, 2, 2) and parameter set 3 has two versionsof (2, 2, 1) and (2, 2, 2). It is noted that the versions are labeled as(Version of Learner 1, Version of Learner 2, and Version of Learner 3).

Therefore, the asynchronous execution facilitation system 100 can createrules for the server to determine when to perform aggregation.

Further, each pull carries an immediate version vector along with thelocal epoch that is checked by the server checking circuit 101. That is,the learners continuously send updates to the server, each time with itsepoch number and the immediate version vector (i.e., (1, 2, 1), (2, 1,2), etc.) that the learner has received from the server. The learner'sepoch number informs the server of the learner's progress. Theintermediate version vector indicates the how old the parameter beingused by the learner is.

The aggregation returning circuit 104 compares the immediate versionvector of the aggregated parameters to a comparison condition and causesthe server to return a new aggregated parameter back to the learner whenthe version vector of the aggregated parameters satisfies the comparisoncondition. The comparison condition is a condition to determine if thecurrent version of the aggregated parameter on the server issignificantly newer than what the learner is using. For example,“significantly newer” may mean more than two epochs newer. If thecomparison condition is not met, the aggregation returning circuit 104allows the learner to continue to perform calculations. Therefore, ifthe most recent received version vector is very close to the currentversion on the parameter server, then there is no need for the server tosend back the current version. This avoids too frequent communicationwhich could congest the network.

The comparison condition can also be based on Δ=N×p.

Therefore, the asynchronous execution facilitation system 100 can alsocreate rules for a server to determine when and what data shall bereturned back to a learner given the learners request (e.g., epoch andlearner's most recent version vector).

FIG. 2 shows a high level flow chart for a method 200 of asynchronousexecution facilitation.

Step 201 checks the aggregation condition based on the intermediateversion vector. The aggregation condition is based on the number ofupdated epochs and checks each pull that carries a version vector alongwith the local epoch.

Step 202 checks the number of updated epochs (Δ) based on the number oflearners (N) that have pushed the updated parameters to the server.

Step 203 triggers for the server to aggregate the pulled parameters fromthe learners when the number of updated epochs equals the number oflearners multiplied by a portion (p) of the learners (e.g., when Δ=N×p).

Step 204 compares the versions vector of the aggregated parameters to acomparison condition and causes the server to return a new aggregatedparameter back to the learner when the version vector of the aggregatedparameters satisfies the comparison condition.

Referring to FIG. 3, FIG. 3 illustrates an exemplary issue withuncoordinated asynchronous execution. As depicted, at epoch 3, theparameters used by Learner 2 (i.e., (2, 2, 2) of parameter set 2) andLearner 3 (i.e., (2, 1, 2) of parameter set 2) contain mismatchedversions.

When each learner conducts its computation, the versions of all theparameter sets used for the computation should be the same, even if theversions are not the latest. Otherwise, the computations may beincongruous with computations by other learners.

With reference to FIG. 5, it is noted that the parameter server is splitup into multiple parameter servers completing a sub-set of theaggregation calculation. Therefore, the “leader” and the “followers”need to be assigned in the coordinated version control system. To electa leader, a Raft leader election protocol, a Zab leader electionprotocol, a Paxos leader election protocol, or the like may be used.Once the “leader” is determined, the remaining servers are the“followers”.

The leader parameter collecting circuit 501 collects the parameter datafrom each of the learners for the leader parameter set at a previousepoch (e.g., epoch 2 of FIG. 3).

Also, the follower parameter collecting circuit 505 collects theparameter data from each of the learners for the follower parameter sets(e.g., epoch 2 of FIG. 3).

The leader aggregated parameter generating circuit 502 generates a newversion of the aggregated parameter set from the collected parameterdata of each learner (i.e., epoch 3 of FIG. 3). It is noted that theleader aggregated parameter generating circuit 502 can generate the newversion of the aggregated parameter set based on the asynchronousexecution facilitation of the system 100 of FIG. 1.

In other words, if parameter set 3 as shown in FIG. 3 is the leader, theleader aggregated parameter generating circuit 502 creates a newaggregated parameter set 3 (2, 2, 1) to be pulled by the learners atepoch 3.

The event broadcasting circuit 503 broadcasts a broadcast eventindicating that a new version of an aggregated parameter set has beencreated by the leader aggregated parameter generating circuit 502. Thebroadcast event includes the new version of the aggregated parameter setof the leader. The broadcasting circuit 503 broadcasts the broadcastevent to the parameter servers within the server cluster.

The broadcast detecting circuit 506 detects the broadcast event andcauses the checking circuit 507 to check the versions that have beencompleted by the learners. The checking circuit 507 checks each of thefollowers to determine if a matching parameter set version has beencreated (e.g., if a (2, 2, 1) parameter set exists in each of thefollowers).

If a matching version of the parameter set to the leader has not beencreated, the follower aggregated parameter generating circuit 508generates a new version of the aggregated parameter set for each of thefollowers to match the version of the leader.

Then, as the server receives a pull request from each of the learners,the leader pull requesting circuit 504 and the follower pull requestingcircuit 509 authorizes the learners to pull the aggregated matchingparameter sets.

For example, as shown in FIG. 3, if the leader is parameter set 3 andthe leader aggregated parameter generating circuit 502 generates the newversion of the aggregated parameter set 3 as (2, 2, 1), the eventbroadcasting circuit 503 will broadcast the broadcast event in which thechecking circuit 507 will check as to whether the followers (i.e.,parameter set 2 and parameter set 3) have finished computations from aprevious epoch such that the follower aggregated parameter generatingcircuit 508 can generate the same version of the aggregated parametersets 2 and 3 of version (2, 2, 1) for each of the followers.

Thus, as shown in FIG. 4, each parameter set 1, 2, and 3 includesversion (2, 2, 1), such that the learners can pull the same aggregatedversion and computations remain consistent. That is, the pull request isnot granted by the server (i.e., by the leader pull requesting circuit504 and the follower pull requesting circuit 509) to return the newparameter set back to the learner until the parameter set versions aresynchronized.

The coordinated version control system 500 therefore can cause each ofthe followers to generate the same version of parameter sets in lockstepmanner with the leader so as to coordinate the computations. Thefollowers each create the new versions of the aggregated parameter setsin an on-demand manner which facilitates version consistently.

As shown in FIG. 9, the leader aggregated parameter generating circuit502 generates a new version of the aggregated parameter set of (2, 2, 1)based on the rules of the asynchronous execution facilitation system100. Based on the broadcast event, the follower aggregated parametergenerating circuit 508 will generate a matching version of theaggregated parameter set of (2, 2, 1) such that the server can satisfy apull request and continue to make computations even if learner 3 has notfinished with version 2 for this particular parameter set. It is notedthat as learner 3 completes version 2, the leader will create a newversion of the aggregated parameter set of (2, 2, 2) and the followerswill follow.

In other words, based on the rules of a new version being created, thesystem 500 can more efficiently move forward with computations.

FIG. 6 shows a high level flow chart for a method 600 of coordinatedversion control.

Step 601 collects the parameter data from each of the learners for theleader parameter set at a previous epoch (e.g., epoch 2 of FIG. 3).

Also, Step 605 collects the parameter data from each of the learners forthe follower parameter sets (e.g., epoch 2 of FIG. 3).

Step 602 generates a new version of the aggregated parameter set fromthe collected parameter data of each learner (i.e., epoch 3 of FIG. 3).It is noted that Step 602 can generate the new version of the aggregatedparameter set based on the asynchronous execution facilitation method200 of FIG. 2.

Step 603 broadcasts a broadcast event indicating that a new version ofan aggregated parameter set has been created by Step 602.

Step 606 detects the broadcast event and causes Step 607 to check theversions that have been completed by the learners. Step 607 checks eachof the followers to determine if a matching parameter set version hasbeen created.

If a matching version of the parameter set to the leader has not beencreated, step 608 generates a new version of the aggregated parameterset for each of the followers to match the version of the leader.

Then, as the server receives a pull request from each of the learners,Step 604 and Step 609 authorizes the learners to pull the aggregatedmatching parameter sets.

With reference to FIG. 7, the version enforcement system 700 can enforceversion consistency implanted from a learner side of distributed machinelearning.

The parameter collecting circuit 701 collects all of the parameter setsfrom the parameter servers.

The version examining circuit 702 determines a version of the parameterset for each of the parameter servers. For example, as shown in FIG. 3at epoch 3, parameter set 1 has version (2, 2, 2), parameter set 2 hastwo versions of (2, 1, 2) and (2, 2, 2) and parameter set 3 has twoversions of (2, 2, 1) and (2, 2, 2). It is noted that the versions arelabeled as (Version of Learner 1, Version of Learner 2, and Version ofLearner 3).

The mismatch detecting circuit 703 detects if a mismatch occurs betweeneach of the parameter sets. If the mismatch detecting circuit 703detects that there is not a mismatch (i.e., each version is the same)then the local parameter updating circuit 704 automatically updates thelocal parameters for the computation by a learner.

For example, as shown in FIG. 3, there is a mismatch in that whenlearner 3 pulls the aggregated parameter sets of parameter set 1, 2, and3, parameter set 1 and parameter set 2 do not include the version of theaggregated parameter set (2, 2, 1) of parameter set 3. Therefore, themismatch detecting circuit 703 detects the mismatch and the determiningcircuit 705 determines a correct version for each of the parameter setsto follow.

The determining circuit 705 can use, for example, a Quorum approach(i.e., which version is most prevalent) to determine which version tofollow. That is, the determining circuit 705 can determine to follow theversion of parameter set 3 of (2, 2, 1), parameter set 2 of (2, 1, 2),or parameter set 1 of (2, 2, 2) being pulled by learner 3 at epoch 3.

Preferably, the determining circuit 705 determines to use the versionthat is the most up to date for each of the learners. In other words,the parameter set of (2, 2, 2) of FIG. 3 would preferably be used andeach of the parameter set 2 and 3 has a match.

After the determining circuit 705 determines which version to follow,the requesting circuit 706 requests that each of the other parametersets produce the same version, such that the learner can pull matchingversions. For example, the requesting circuit 706 can request thatparameter set 1 and parameter set 2 produce version (2, 2, 1).

When parameter set 1 receives the request for the matching version,parameter set 1 and parameter set 2 would produce version (2, 2, 1) byusing the cached version of learner 3 version 1 and then replying to therequest with this version.

In requesting the matching version, it is noted that the parameter setmay need to wait for a new parameter set to be pushed to the server.That is, if parameter set 2 did not include (2, 2, 2) but only included(2, 1, 2), the requesting circuit 706 would request that parameter set 2wait for version 2 of the learner 2 to be completed.

Because of the above situation in which sometimes the version has notyet reached the server and is thus not in the cache, the retryingcircuit 707 will retry to retrieve the version requested for apredetermined number of attempts or a predetermined time.

The success checking circuit 708 verifies that the requested version hasbeen retrieved from each of the other parameter sets within thepredetermined number of attempts or the predetermined time.

If the requested version is verified by the success checking circuit708, the local parameter updating circuit 704 automatically updates thelocal parameters for the computation by a learner with the matchingversions of the aggregated parameter sets.

However, if the success checking circuit 708 cannot verify that thematching version is retrieved within the predetermined number ofattempts or the predetermined time, the time out determining circuit 709determines that the current version cannot be retrieved and will attemptto use a different version as determined by the determining circuit 705.

As discussed above, the determining circuit 705 will preferably choosethe most up-to-date version from the parameter set to have the otherparameter sets match. Therefore, the time-out determining circuit 709will alleviate an issue where each of the other learners are too farbehind in computations and cannot catch up to the most current version.Therefore, the time out determining circuit 709 will cause thedetermining circuit 705 to pick an “out of date version” such as (2, 2,1) with the Learner 3 still being on the first version and having theparameter set 1 and parameter set 2 produce an out-of-date aggregatedversion from the cache.

Thus, the version enforcement system 100 can allow for calculations tostill advance to reduce system slow down but stil ensure that eachlearner is using the same version in the computations to avoid nullreturns.

Therefore, as shown in FIG. 4, each of the learners pulls a matchingversion of the aggregated parameter set from each of the parameter setsas a result of the version enforcement system 700.

FIG. 8 shows a high level flow chart for a method 800 of versionenforcement.

Step 801 collects all the parameter sets.

Step 802 determines a version of the parameter set for each of theparameter servers. For example, as shown in FIG. 3 at epoch 3, parameterset 1 has version (2, 2, 2), parameter set 2 has two versions of (2, 1,2) and (2, 2, 2) and parameter set 3 has two versions of (2, 2, 1) and(2, 2, 2). It is noted that the versions are labeled as (Version ofLearner 1, Version of Learner 2, and Version of Learner 3).

Step 803 determines if there is a mismatch between the version selectedby Step 802 and the other versions of the other parameter sets. If no,then the method proceed to Step 804 in which Step 804 automaticallyupdates the local parameters for the computation by a learner.

If YES for Step 803, the method 800 starts a resolution and proceeds toStep 805. Step 805 determines a correct version for each of theparameter sets to follow.

Step 806 sends requests to the other parameter sets responsible for thesets for all the sets that mismatch with the determined correct versionto follow. That is, Step 806 requests that each of the other parametersets produce the same version such that the learner can pull matchingversions.

Step 807 retries to retrieve the version requested for a predeterminednumber of attempts or a predetermined time from Step 806.

Step 808 verifies that the requested version has been retrieved fromeach of the other parameter sets within the predetermined number ofattempts or the predetermined time.

If Step 808 determines YES, then the method proceeds to Step 804 becauseno mismatch exists.

If Step 808 determines NO, Step 809 gives up the resolution of thecurrent version and uses an out of date version to request from theother parameter sets.

Exemplary Hardware Aspects, using a Cloud Computing Environment

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client circuits through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings,

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 10, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10, there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop circuits, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or circuits, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingcircuits that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage circuits.

As shown in FIG. 10, computer system/server 12 in cloud computing node10 is shown in the form of a general-purpose computing circuit. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externalcircuits 14 such as a keyboard, a pointing circuit, a display 24, etc.;one or more circuits that enable a user to interact with computersystem/server 12; and/or any circuits (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing circuits. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,circuit drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 11, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing circuits used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingcircuit. It is understood that the types of computing circuits 54A-Nshown in FIG. 8 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized circuit over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 12, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 11) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 12 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage circuits 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and data store software68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide prearrangement for, and procurement of cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, more particularly relative to thepresent invention, the asynchronous execution facilitation system 100,the coordinated version control system 500, and the version enforcementsystem 700 described herein.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A version enforcement system having a server sideand a client side including parameter servers having a plurality ofparameter sets and a plurality of learners communicating with theplurality of parameter sets, the system comprising: a parametercollecting circuit configured to collect all of the parameter sets fromthe parameter servers; a version examining circuit configured todetermine a version of the parameter set for each of the parameterservers; a mismatch detecting circuit configured to detects if amismatch occurs between each of the parameter sets; and a localparameter updating circuit configured to automatically updates localparameters for a computation by a learner if the mismatch detectingcircuit detects that there is not a mismatch.
 2. A version enforcementmethod having a server side and a client side including parameterservers having a plurality of parameter sets and a plurality of learnerscommunicating with the plurality of parameter sets, the methodcomprising: collecting all of the parameter sets from the parameterservers; determining a version of the parameter set for each of theparameter servers; detecting if a mismatch occurs between each of theparameter sets; and automatically updating local parameters for acomputation by a learner if the mismatch detecting detects that there isnot a mismatch.
 3. An asynchronous execution facilitation system havinga server side and a client side including parameter servers having aplurality of parameter sets and a plurality of learners communicatingwith the plurality of parameter sets, comprising: collecting all of theparameter sets from the parameter servers; determining a version of theparameter set for each of the parameter servers; detecting if a mismatchoccurs between each of the parameter sets; and automatically updatinglocal parameters for a computation by a learner if the mismatchdetecting detects that there is not a mismatch.